Assessment of Sleep Quality and Sleep Disordered Breathing Based on Cardiopulmonary Coupling

ABSTRACT

An assessment of sleep quality and sleep disordered breathing is determined from the cardiopulmonary coupling between two physiological data series. In an embodiment, an R-R interval series is derived from an electrocardiogram (ECG) signal. The normal beats from the R-R interval series are extracted to produce a normal-to-normal (NN) interval series. The amplitude variations in the QRS complex are used to extract to a surrogate respiration signal (i.e., ECG-derived respiration (EDR)) that is associated with the NN interval series. The two series are corrected to remove outliers, and resampled. The cross-spectral power and coherence of the two resampled signals are calculated over a plurality of coherence windows. For each coherence window, the product of the coherence and cross-spectral power is used to calculate coherent cross power. Using the appropriate thresholds for the coherent cross power, the proportion of sleep spent in CAP, non-CAP, and wake and/or REM are determined.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.10/846,945, filed May 17, 2004, which issued as U.S. Pat. No. 7,324,845,on Jan. 29, 2008, the entire disclosure of which is herein incorporatedby reference.

STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH OR DEVELOPMENT

Part of the work performed during development of this invention utilizedU.S. Government funds. The U.S. Government has certain rights in thisinvention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to analyzing physiologic data,and more specifically, to non-invasively assessing sleep pathology andphysiology from coherence measurements.

2. Related Art

At least five percent of the general population suffers from medicallysignificant sleep disorders, the most common being sleep-disorderedbreathing (also known as sleep apnea). As a major public health concern,sleep disorders contribute to excessive daytime sleepiness and theassociated risks of driving accidents, hypertension, heart attacks,strokes, depression, and attention deficit disorders. The prevalence ofsleep disorders is much higher (exceeding thirty percent) in selectpopulations such as, individuals having obesity, congestive heartfailure, diabetes, and renal failure.

Conventional diagnostic systems for detecting sleep disorders typicallyinvolve complex multiple channel recordings in a sleep laboratory andlabor intensive scoring, which collectively lead to substantial expenseand patient discomfort. An example of a conventional sleep diagnosticsystem is a full polysomnograph. Polysomnography is the gold standardfor detection and quantification of sleep-disordered breathing, andincludes sleep staging, scoring of respiratory abnormality (e.g.,apneas, hypopneas, flow-limitation, periodic breathing, and desaturationepisodes), and limb movements. Typical markers of sleep disorderseverity are the sleep fragmentation index, the apnea-hypopnea index,the respiratory disturbance index, an arousal frequency or index, andthe oxygen desaturation index.

One of the many limitations of conventional sleep diagnostic systems isthe dependence on tedious manual scoring of “events” based onphysiologically arbitrary criteria. Only a moderate correlation can befound between these events and cognitive and cardiovascular outcomes. Assuch, conventional systems leave a significant amount of unexplainedvariance in effect, and fail to adequately describe the physiologicimpact of sleep disorders. Therefore, a quantitative measure thatevaluates the impact of sleep disorders on sleep physiology could beuseful in clarifying some of the unexplained variance. A continuousbiomarker of physiological state may be particularly useful to followtreatment effects. A continuous biomarker may also be useful todiscriminate those in whom the seemingly subtle sleep disorder diseaseis physiologically disruptive. Such physiologically disruptive settingsinclude primary snoring, which in adults, is associated with excessivesleepiness, and in children, is associated with inattentive and/orhyperactive behaviors.

Presently, rapid and accurate throughput of sleep diagnostics does notexist, despite the development of limited forms of sleep testing thatinclude various combinations of airflow, respiratory effort,electrocardiogram (ECG), and oximetry. This is especially problematic inconditions such as congestive heart failure and chronic renal failure,where severe and complex forms of sleep apnea may adversely affect bothmortality and morbidity. Since conventional sleep studies are soexpensive, information on sleep effects are typically limited in thepre-approval assessments of drugs used in neurological and psychiatricpractice.

Therefore, a need exists to develop a technology that can provide asimple, inexpensive, repeatable measure of the presence and impact of avariety of sleep disruptive stimuli (such as noise, pain, drugs, mooddisorders, disordered breathing) on sleep state physiology andstability.

SUMMARY OF THE INVENTION

The present invention provides a method, system and computer programproduct for performing a quantitative analysis of cardiopulmonarycoupling between two physiological signals to detect and evaluate sleepphysiology and pathology. According to embodiments of the presentinvention, an R-R interval series is combined with a correspondingrespiration signal to determine the coherent cross-power of these twosignals.

In an embodiment, an electrocardiogram (ECG) signal is used to derivethe R-R interval series. In another embodiment, the R-R interval seriesis acquired from another type of physiological signal representing heartrate dynamics within a subject. Thus, the heart rate physiologicalsignal can be derived from, for example, ECG, blood pressure, pulseDoppler flow (e.g., sensed via ultrasound recording), ECG signal fromanother electrical signal (e.g., electroencephalographic (EEG)), or thelike.

In an embodiment, the same ECG is used to derive a surrogate respirationsignal (i.e., ECG-derived respiration (EDR)). In another embodiment, therespiration signal is acquired from another type of physiological signalrepresenting respiration dynamics in the subject. Thus, the respirationphysiological signal can be derived from, for example, ECG, nasalthermistor, nasal flow, chest band, abdominal band, or the like.

In an embodiment using ECG to derive the R-R intervals and associatedrespiration signal, an automated beat detection routine or functiondetects beats from the ECG and classifies them as either normal orectopic. In addition, amplitude variations in the QRS complex due toshifts in the cardiac electrical axis relative to the electrodes duringrespiration are determined. From these amplitude variations, the EDR isobtained.

A time series of normal-to-normal (NN) intervals and the time series ofthe EDR associated with the NN intervals are then extracted from the R-Rinterval series. Outliers due to false detections or missed detectionsare removed using a sliding window average filter, and the resulting NNinterval series and its associated EDR are resampled.

The cross-spectral power and coherence of the two resampled signals arecalculated over a predefined window using the Fast Fourier Transform(FFT) or Discrete Fourier Transform (DFT). The coherence window isthereafter advanced, and the FFT or DFT calculations are repeated untilthe entire NN interval series and associated EDR series have beenanalyzed.

For each coherence window, the product of the coherence andcross-spectral power is used to calculate the ratio of coherent crosspower in the low frequency (i.e., 0.01-0.1 Hz.) band to that in the highfrequency (i.e., 0.1-0.4 Hz.) band. An excess of coherent cross power inthe low frequency band is associated with periodic characteristics inmultiple physiological systems. In the electroencephalogram, this isreflected by the morphology called Cyclic Alternating Pattern (CAP),characterized by the presence of repetitive activation complexes(K-complexes, delta waves, variable bursts of alpha or beta rhythms)alternating with relatively quiescent “B” or baseline phases. In therespiratory system, periodically recurring cycles of abnormalrespiration that may or may not be obstructed are seen.

A preponderance of coherent cross-power in the high frequency band isassociated with normal sinus arrhythmia and is the ECG marker of thisstate. There is also a close correlation between high-frequency coherentcross-power and the EEG morphology called non-CAP.

Using the ratio of power in the very low frequency (i.e., 0-0.01 Hz)band to the combined coherent cross power in the low and high frequencybands allows detection of wake and/or REM. An excess of coherentcross-power in the very low frequency band tends to be associated withwake and/or REM.

As such, the present invention provides an ECG-based measure of sleepquality and sleep disordered breathing using the CAP/non-CAPphysiological concept. Using the appropriate thresholds for the coherentcross power ratios, the proportion of sleep spent in CAP, non-CAP, andwake and/or REM can be determined.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form partof the specification, illustrate the present invention and, togetherwith the description, further serve to explain the principles of theinvention and to enable one skilled in the pertinent art(s) to make anduse the invention. In the drawings, generally, like reference numbersindicate identical or functionally or structurally similar elements.Additionally, generally, the leftmost digit(s) of a reference numberidentifies the drawing in which the reference number first appears.

FIG. 1 illustrates an operational flow for detecting cardiopulmonarycoupling according to an embodiment of the present invention.

FIG. 2 illustrates an operational flow for using ECG-derived respirationto quantify cardiopulmonary coupling according to an embodiment of thepresent invention.

FIG. 3 illustrates an operational flow for removing outliers from a datainterval series according to an embodiment of the present invention.

FIG. 4 illustrates an operational flow for calculating cross-spectralpower and coherence according to an embodiment of the present invention.

FIG. 5 illustrates an example computer system useful for implementingportions of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

According to embodiments of the present invention, a method, system, andcomputer program product is provided to perform a quantitative analysisof cardiopulmonary coupling between two physiological signals to detectand evaluate sleep physiology.

Sleep is a complex state characterized by cycling stages (i.e., rapideye movement (REM) sleep and non-REM sleep) and a sequence ofprogressive and regressive depths (i.e., stages I to IV in non-REMsleep). There is also a stability dimension that has been recognized,but not generally applied in clinical practice, based on the concept ofcyclic alternating pattern (CAP) in non-REM sleep. CAP-type non-REMsleep is unstable, whereas, non-CAP non-REM sleep is a restful,non-aroused state with a stabilizing influence.

The distribution of stages and states of sleep can be altered bynumerous sleep disrupting extrinsic factors (e.g., noise, heat, cold,vibration, barotraumas, motion, gravitational stress, etc.) andintrinsic factors (e.g., disordered breathing, pain, seizures, restlesslegs, periodic limb and related movements, etc.). More importantly,these factors all induce CAP, a state characterized by periodic behaviorin multiple measures (such as, brain electrical activity, heart rate,respiration, and blood pressure). In the non-CAP state, physiologicalstability exists.

Sleep disordered breathing (SDB) is associated with the emergence ofrelatively low frequency (i.e., 0.01 to 0.1 Hertz (Hz)) periodicbehavior in multiple physiologic systems, such as respiration, heartrate, and electroencephalographic (EEG) activity. These pathologicaloscillations represent periods of physiologically unstable sleepbehavior and low frequency coupling of respiration-driven ECGvariability. In this state, the EEG pattern typically displays CAPattributes.

In contrast, periods of stable breathing are associated with the non-CAPEEG pattern and high frequency (i.e., 0.1 to 0.4 Hz) coupling betweenrespiration and beat-to-beat heart rate variability (e.g., respiratorysinus arrhythmia). In patients with SDB, spontaneous and relativelyabrupt transitions tend to occur between these two states.

As described in greater detail below, the present invention providestechniques and/or methodologies for quantifying cardiopulmonarycoupling, which shows a strong correlation with CAP and non-CAP states.Accordingly, the present invention provides a biomarker of sleepphysiology and pathology, such as the percentage of sleep spent inperiods of unstable sleep behavior.

Referring to FIG. 1, flowchart 100 represents the general operationalflow of an embodiment of the present invention. More specifically,flowchart 100 shows an example of a control flow for detectingcardiopulmonary coupling from a subject (such as, a patient,test/laboratory subject, or the like).

The control flow of flowchart 100 begins at step 101 and passesimmediately to step 103. At step 103, a set of interval respiration data(referred to herein as a “respiration series”) is accessed from aphysiological signal. The physiological signal can be anelectrocardiogram (ECG or EKG), from which a surrogate respiratorysignal is obtained, as described in greater detail below. However, thephysiological signal can be any type of signal representing respirationdynamics in the subject. As such, the respiration data series can bederived from, for example, a nasal thermistor, forced oscillation,acoustic reflectance techniques, nasal-cannula pressure transducersystem, impedance/inductance/piezo chest and/or abdominal effort band,or the like.

At step 106, a set of interval heart rate data (referred to herein as a“R-R interval series”) is accessed from a physiological signal. In anembodiment, the heart rate physiological signal is the samephysiological signal that provides the respiration interval signal thatis described above at step 103. In another embodiment, the heart ratephysiological signal is distinct from the physiological signal thatprovides the respiration signal. Moreover, the heart rate physiologicalsignal can be the same type of signal (e.g., both being ECG) or adifferent type of signal (e.g., ECG for the heart rate interval series,and nasal-cannula pressure-transducer nasal thermistor flow for therespiration series) as the physiological signal that provides therespiration signal.

Accordingly, the heart rate physiological signal is any type of signalthat enables the derivation of a series of heart rate interval data(i.e., R-R intervals or R-R equivalent intervals). Thus, the heart ratephysiological signal can be any type of signal representing heart ratedynamics in the subject. Such signal can be derived from, for example,ECG, blood pressure, pulse Doppler flow (e.g., sensed via ultrasoundrecording), ECG signal from another electrical signal (e.g., EEG), orthe like.

Regardless of their source(s), the heart rate interval series andrespiration series must be temporally aligned to determine thecardiopulmonary coupling. In an embodiment, the normal sinus (N) beatsare selected from the heart rate interval series to produce a series ofnormal-to-normal (NN) heart rate data (referred to herein as an “NNinterval series”). The respiration series would therefore be temporallyaligned with the NN interval series.

At step 109, cross-spectral power and coherence is calculated using boththe heart rate interval series (or NN interval series) and respirationseries. At step 112, the product of the coherence and cross-spectralpower calculations are taken to derive a set of coherent cross powercalculations. At step 115, the coherent cross power calculations areused to determine the cardiopulmonary coupling. As described in greaterdetail below, the cardiopulmonary coupling can be compared with one ormore detection thresholds for diagnostic evaluations, such as SDBscreening or the like. Afterwards, the control flow ends as indicated atstep 195. As such, the present invention provides for a fully automatedquantitative analysis of cardiopulmonary coupling that can be used, forexample, to screen for SDB, assess physiological impacts of SDB, and/ormonitor the therapeutic effects of different approaches to treating SDB,or the like.

Referring to FIG. 2, flowchart 200 represents the general operationalflow of another embodiment of the present invention. More specifically,flowchart 200 shows an example of a control flow for quantifyingcardiopulmonary coupling by using ECG-derived respiration (EDR) tointegrate R-R variability and fluctuations in cardiac electrical axisassociated with respiration.

The control flow of flowchart 200 begins at step 201 and passesimmediately to step 203. At step 203, an ECG signal or set of ECG datais accessed. In an embodiment, surface ECG data is retrieved from astorage medium. In another embodiment, surface ECG data is obtaineddirectly from an ECG monitoring device. For example, ECG data can beobtained, in real time or otherwise, from a Holter monitor or recordingfrom an ECG monitor available from GE Marquette Medical Systems(Milwaukee, Wis.).

In an embodiment, a single or two lead ECG signal is obtained directlyfrom an ECG monitoring device. If using one lead, the skin sensor orelectrode should be placed at or near the V2 chest position. If usingtwo leads, it is preferable to position the electrodes relativelyorthogonal to each other. However, other chest positions can be used toobtain the single or two lead ECG data.

At step 206, the ECG data is analyzed to detect and classify heart beats(i.e., R-R intervals), from the ECG data, as being normal (N) orectopic. In an embodiment, an automated beat detection and annotationroutine or function is used to process digitized ECG data from a Holterrecording. The routine detects and classifies (i.e., labels) the heartbeats from the digitized data. The output is a time series of annotatedheart beats (i.e., “R-R interval series”). This series is then processedto retain only normal-to-normal sinus beats (i.e., “NN intervalseries”).

At step 209, the ECG data is analyzed to extract a time series of QRSamplitude variations (i.e., “EDR series”) that are associated with theNN interval series. The amplitude variations in the QRS complex (fromthe normal beats) are due to shifts in the cardiac electrical axisrelative to the electrodes during respiration. From these amplitudevariations, a surrogate respiratory signal (i.e., EDR) is derived. In anembodiment, the same function or routine used to extract the NN intervalseries is also used to measure the QRS amplitudes for the heart beats,and produce a continuous EDR signal.

At step 212, the NN interval series and the associated EDR series areanalyzed to detect and/or remove any outliers due to false detections ormissed detections. For example, during step 206, the automated beatdetection function may generate a false detection or fail to detect anormal data point for the NN interval series (and, hence the associatedEDR series). In an embodiment, a sliding window average filter is usedto remove the outliers, as described with reference to FIG. 3 in greaterdetail below.

At step 215, the filtered NN interval series and the filtered EDR seriesare resampled. The resampling rate depends on the subject class, and isselected to optimize the data series for the spectra calculationsdescribed in the following steps. For example, for human subjects, bothseries are resampled at two Hz. For premature or neonatal subjects (whoare approximately less than one year of age), a four Hz resampling rateis used because neonatal infants typically have a heart rate that isapproximately twice the rate of an adult. For nonhuman subjects (suchas, laboratory mice), a twenty Hz resampling rate is used.

At step 218, cross-spectral power and coherence are calculated foroverlapping windows of data selected from, both, the resampled NNinterval series and the resampled EDR series. In an embodiment, a FastFourier Transform (FFT) is used to compute the cross-spectral power andcoherence calculations for the two signals. As described in greaterdetail below with reference to FIG. 4, a plurality of overlappingwindows is used to perform the FFT computations.

At step 221, a data series of “coherent cross power” are calculated fromthe product of the coherence and cross-spectral power calculations fromstep 218. For each window, the product of the coherence andcross-spectral power is used to calculate the coherent cross power foreach window of data.

If a coherent cross power calculation falls within the range of 0 to0.01 Hz, it is considered to be within the very low frequency (VLF)band. If the coherent cross power falls within the range of 0.01 to 0.1Hz, it is considered to be within the low frequency (LF) band. If thecoherent cross power falls within the range of 0.1 to 0.4 Hz, it isconsidered to be within the high frequency (HF) band. When making theVLF, LF, and HF determinations, at least two frequency bins in each bandis used to make the calculations. The two bins having the greatest powerare used.

At step 224, “cardiopulmonary coupling” is determined from the coherentcross power. The cardiopulmonary coupling can be compared with adetection threshold for a variety of diagnostic applications. Forexample, the cardiopulmonary coupling can be compared with a predefineddetection threshold to detect or evaluate sleep qualities, such as CAPactivity, non-CAP activity, wake and/or REM activity, or the like.

For CAP activity, the coherent cross power is used to calculate a ratioof coherent cross power in the LF band to that in HF. An excess ofcoherent cross power in the low frequency band is associated withperiodic behaviors in the EEG (i.e., CAP) and periodic patterns ofbreathing. In an embodiment, the detection thresholds are 0.2 for theminimum LF power, and 2.0 for the minimum LF/HF ratio.

For non-CAP, the coherent cross power is used to calculate a ratio ofcoherent cross power in the LF band to that in HF. An excess of coherentcross power in the high frequency band is associated with physiologicrespiratory sinus non-CAP) arrhythmia, stable non-periodic breathingpatterns and non-CAP EEG. In an embodiment, the detection thresholds are0.02 for the minimum HF power, and 1.5 for the maximum LF/HF ratio.

For wake and/or REM activity, the ratio of coherent cross power in theVLF band to combined power in LF and HF allows detection of wake and/orREM. An excess of coherent cross power in the VLF band tends to beassociated with wake and/or REM. In an embodiment, the detectionthresholds are 0.05 for the minimum VLF power and 0.2 for the maximumVLF-to-(LF+HF) ratio.

In those with significant sleep-disordered breathing, REM sleep usuallyhas coherent cross power characteristics very similar and oftenindistinguishable from CAP physiology. Cardiopulmonary coupling can alsobe used to detect or evaluate SDB and its impact on sleep (e.g., theproportion of sleep time spent in CAP). For SDB, the coherent crosspower is used to calculate a ratio of coherent cross power in the LFband to that in HF. An excess of coherent cross power in the LF band isassociated with CAP, periodic respiration, and EEG CAP. In anembodiment, the detection thresholds are 0.2 for the minimum LF power,and 50 for the minimum LF/HF ratio.

Cardiopulmonary coupling can also be used to assess physiologicalimpacts of the aforesaid sleep qualities, SDB, or the like. Thecardiopulmonary coupling can also be used monitor the therapeuticeffects of different approaches to treating the aforesaid sleepqualities, SDB, or the like. Hence, as described above, the presentinvention combines the use of mechanical and autonomic effects of, forexample, SDB on ECG parameters. Upon completion of the cardiopulmonarycoupling quantification and analyses, the control flow ends as indicatedat step 295.

As described above with reference to step 212, in an embodiment, the NNinterval series and their associated EDR series are analyzed to detectand/or remove outliers due to false detections or missed detections.Referring to FIG. 3, flowchart 300 represents the general operationalflow of an embodiment of the present invention for removing outliersfrom a data series. More specifically, flowchart 300 shows an example ofa control flow for using a sliding window average to filter outliers.

The control flow of flowchart 300 begins at step 301 and passesimmediately to step 303. At step 303, all data points in the NN intervalseries that are determined to be physiologically impossible orinfeasible are excluded a priori. The a priori exclusion thresholddiffers according to the subject class. In an embodiment, the a prioriexclusion threshold is an NN interval less than 0.4 seconds or an NNinterval greater than 2.0 seconds.

At step 306, the window size is set to begin the averaging. In anembodiment, the window size is set for forty-one data points. Next, afirst window (i.e., a first set of forty-one data points) are selectedfrom the NN series.

At step 309, a reference point is selected from within the window. Forexample, the middle (or twenty-first) data point can be selected as thereference point.

At step 312, the twenty points preceding the reference point and thetwenty points following the reference point are averaged. At step 315,it is determined whether the reference point equals or exceeds apredefined threshold based on the average value calculated at step 312.In an embodiment, the reference point exclusion threshold is set attwenty percent.

At step 318, the reference point is excluded from the data series if itsatisfies the reference point exclusion threshold. For example, if thereference point exclusion threshold is set at twenty percent and thereference point deviates twenty percent or more from the averaged value,the reference point is excluded.

At step 321, the reference point remains in the data series if it doesnot satisfy the reference point exclusion threshold. For example, if thereference point exclusion threshold is set at twenty percent and thereference point deviates less that twenty percent of the averaged value,the reference point remains in the data set.

At step 324, the data series is checked to determine whether additionaldata is available for processing. If additional data is available, atstep 327, the window is moved up one data point and the control flowreturns to step 309 to repeat the search for outliers. Otherwise, thecontrol flow ends as indicated at step 395.

The above parameters (i.e., a priori exclusion threshold, window size,reference point exclusion threshold) are provided by way of example andcan be adjusted to optimize the quantification of cardiopulmonarycoupling as desired by the operator.

As described above with reference to step 218, cross-spectral power andcoherence are calculated for overlapping windows of data selected from,both, the NN interval series and their associated EDR series. In anembodiment, the cross-spectral power and coherence of these two signalsare calculated over a 1,024 sample (e.g., approximately 8.5 minute)window using FFT applied to three overlapping 512 sample sub-windowswithin the 1,024 coherence window. Referring to FIG. 4, flowchart 400represents the general operational flow of an embodiment of the presentinvention for calculating cross-spectral power and coherence. Morespecifically, flowchart 400 shows an example of a control flow for usingFFT to calculate cross-spectral power and coherence of two data series.

The control flow of flowchart 400 begins at step 401 and passesimmediately to step 403. At step 403, a window of data is selected.

At step 406, FFTs are performed within the selected window. In anembodiment, three FFTs are performed within the selected window. A firstFFT is performed at the beginning, a second transform at the middle, anda final transform at the end of the selected window. For each FFT, thesize of the frequency bin is 512 data points. The intra-windowincrements are 256 points for the first, second and third FFT. Thus, thesub-windows overlap by fifty percent.

At step 409, the individual results of the FFT calculations are combinedand averaged to determine a cross-spectral power and coherence value. Inother words, for each 1,024 window, the three FFT calculations arecombined and averaged to determine the cross-spectral power andcoherence for a window of data.

At step 412, it is determined whether additional data are available forfurther FFT calculations. If additional data are available, at step 415,the next window of data is selected and the control flow returns to step406 to apply FFT to three overlapping 512 sample sub-windows within theselected 1,024 coherence window. In other words, the previous 1,024window (selected at step 403) is advanced by 256 samples (e.g.,approximately 2.1 minutes) and three FFT calculations are performed.

Upon completion of the calculations for the entire NN and EDR dataseries, the control flow ends as indicated at step 495.

FIGS. 1-4 are conceptual illustrations allowing an explanation of thepresent invention. It should be understood that embodiments of thepresent invention could be implemented in hardware, firmware, software,or a combination thereof. In such an embodiment, the various componentsand steps would be implemented in hardware, firmware, and/or software toperform the functions of the present invention. That is, the same pieceof hardware, firmware, or module of software could perform one or moreof the illustrated blocks (i.e., components or steps).

The present invention can be implemented in one or more computer systemscapable of carrying out the functionality described herein. Referring toFIG. 5, an example computer system 500 useful in implementing thepresent invention is shown. Various embodiments of the invention aredescribed in terms of this example computer system 500. After readingthis description, it will become apparent to one skilled in the relevantart(s) how to implement the invention using other computer systemsand/or computer architectures.

The computer system 500 includes one or more processors, such asprocessor 504. The processor 504 is connected to a communicationinfrastructure 506 (e.g., a communications bus, crossover bar, ornetwork).

Computer system 500 can include a display interface 502 that forwardsgraphics, text, and other data from the communication infrastructure 506(or from a frame buffer not shown) for display on the display unit 530.

Computer system 500 also includes a main memory 508, preferably randomaccess memory (RAM), and can also include a secondary memory 510. Thesecondary memory 510 can include, for example, a hard disk drive 512and/or a removable storage drive 514, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. The removable storagedrive 514 reads from and/or writes to a removable storage unit 518 in awell-known manner. Removable storage unit 518, represents a floppy disk,magnetic tape, optical disk, etc. which is read by and written toremovable storage drive 514. As will be appreciated, the removablestorage unit 518 includes a computer usable storage medium having storedtherein computer software (e.g., programs or other instructions) and/ordata.

In alternative embodiments, secondary memory 510 can include othersimilar means for allowing computer software and/or data to be loadedinto computer system 500. Such means can include, for example, aremovable storage unit 522 and an interface 520. Examples of such caninclude a program cartridge and cartridge interface (such as that foundin video game devices), a removable memory chip (such as an EPROM, orPROM) and associated socket, and other removable storage units 522 andinterfaces 520 which allow software and data to be transferred from theremovable storage unit 522 to computer system 500.

Computer system 500 can also include a communications interface 524.Communications interface 524 allows software and data to be transferredbetween computer system 500 and external devices. Examples ofcommunications interface 524 can include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface524 are in the form of signals 528 which can be electronic,electromagnetic, optical, or other signals capable of being received bycommunications interface 524. These signals 528 are provided tocommunications interface 524 via a communications path (i.e., channel)526. Communications path 526 carries signals 528 and can be implementedusing wire or cable, fiber optics, a phone line, a cellular phone link,an RF link, free-space optics, and/or other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage unit 518, removable storage unit 522, a hard disk installed inhard disk drive 512, and signals 528. These computer program productsare means for providing software to computer system 500. The inventionis directed to such computer program products.

Computer programs (also called computer control logic or computerreadable program code) are stored in main memory 508 and/or secondarymemory 510. Computer programs can also be received via communicationsinterface 524. Such computer programs, when executed, enable thecomputer system 500 to implement the present invention as discussedherein. In particular, the computer programs, when executed, enable theprocessor 504 to implement the processes of the present invention, suchas the various steps of methods 100, 200, 300, and 400, for example,described above. Accordingly, such computer programs representcontrollers of the computer system 500.

In an embodiment where the invention is implemented using software, thesoftware can be stored in a computer program product and loaded intocomputer system 500 using removable storage drive 514, hard drive 512,interface 520, or communications interface 524. The control logic(software), when executed by the processor 504, causes the processor 504to perform the functions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine so as to perform the functions described herein will beapparent to one skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the art (including the contents of thedocuments cited and incorporated by reference herein), readily modifyand/or adapt for various applications such specific embodiments, withoutundue experimentation, without departing from the general concept of thepresent invention. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance presented herein, in combination with the knowledge of oneskilled in the art.

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to one skilled in therelevant art(s) that various changes in form and detail can be madetherein without departing from the spirit and scope of the invention.Thus, the present invention should not be limited by any of theabove-described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

1. A method of detecting cardiopulmonary coupling between heart ratevariability and respiration of a subject, comprising: accessing a firstseries of physiological data representing respiration dynamics of thesubject; accessing a second series of physiological data representingheart rate dynamics of the subject, said second series being temporallyaligned with said first series; and detecting cardiopulmonary couplingbetween said first series and said second series.